A full‐reference stereoscopic image quality assessment index based on stable aggregation of monocular and binocular visual features. Issue 8 (28th January 2021)

• Record Type: Journal Article
• Authors: Si, Jianwei; Yang, Huan; Huang, Baoxiang; Pan, Zhenkuan; Su, Honglei
• Is Part Of: IET image processing. Volume 15:Issue 8(2021)
• Page Start: 1629
• Page End: 1643
• Publication Date: 2021-01-28
• Subjects: Image recognition -- Computer vision and image processing techniques; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12132
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: In stereoscopic image quality assessment, human visual system has been universally taken into account to detect perceptual characteristics. A novel full‐reference stereoscopic image assessment metric by considering both monocular and binocular visual features of human visual system is proposed. In particular, a new region segmentation algorithm is firstly proposed to divide 3D images into occluded and non‐occluded regions. The just noticeable difference model is employed on the occluded regions to formulate the monocular vision, while the binocular just noticeable difference model is applied to the non‐occluded regions to reveal the binocular vision of the human visual system. In the proposed region segmentation, disparity information and Euclidean distance between stereo pairs are both adopted to solve the unstable segmentation problem of traditional methods. A new pooling strategy based on global edge features is then presented to aggregate the just noticeable difference and binocular just noticeable difference evaluation maps. In addition, some local image features as supplementary of just noticeable difference to describe visual characteristics of the human visual system are also extracted. Finally, an overall quality score is calculated based on the above‐mentioned features to measure the visual quality of distorted stereo pairs. Experimental results show that the proposed metric achieves high consistency with the human visual system, and outperforms state‐of‐the‐art algorithms on stereoscopic image quality assessment.